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Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter. # Cellchat
source("./tianfengRwrappers.R")library(CellChat)
CellChat
配体-受体+辅因子
#推断细胞间通信网络，不要使用并行计算！
cor <- computeCommunProb(cor)
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|====================================================================================================| 100%#推断细胞间通信网络，不要使用并行计算！
cor <- computeCommunProb(cor)
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|====================================================================================================| 100%#若细胞群中只有少数细胞出现联系(<10个)，则过滤掉它们
cor <- filterCommunication(cor,min.cells = 10)
#提取表达网络矩阵
df.net <- subsetCommunication(cor)
#推断信号通路
cor <- computeCommunProbPathway(cor)
#合并通信网络数据
cor <- aggregateNet(cor)write.csv(df.net,"./datatable/cellchat_res.csv", row.names = F)cellchat可视化
cor <- readRDS("CA2_cellchat.rds")
# cor@idents <- plyr::revalue(cor@idents, c("Mod_SMC"="Fibroblast", "SMC_0"="SMC1","SMC_1"="Fibromyocyte",
# "SMC_2"="Pericyte","SMC_3"="SMC2"))
table(cor@idents)
SMC1 Fibromyocyte Pericyte SMC2 EC FB
3430 2294 1590 1021 6092 1224
Macrophage T cell NK cell Neural progenitor B cell Mast cell
9832 12245 4125 366 2206 430 groupSize <- as.numeric(table(cor@idents))
netVisual_circle(cor@net$weight, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Interaction weights/strength")
netVisual_heatmap(cor, measure = "weight", signaling = NULL, color.heatmap = c("#f1f1f1", "#ff2121"))Do heatmap based on a single object 
#一次分析不同细胞亚群
mat <- cor@net$weight
for (i in 1:nrow(mat)) {
mat2 <- matrix(0, nrow = nrow(mat), ncol = ncol(mat), dimnames = dimnames(mat))
mat2[i, ] <- mat[i, ]
netVisual_circle(mat2, vertex.weight = groupSize, weight.scale = T, edge.weight.max = max(mat), title.name = rownames(mat)[i])
}
# pheatmap(mat, display_numbers = FALSE, number_color ="black", cluster_rows = FALSE,
# cluster_cols = FALSE, color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(400), border_color = NA)
table(cor@idents)
SMC1 Fibromyocyte Pericyte SMC2 EC FB
3430 2294 1590 1021 6092 1224
Macrophage T cell NK cell Neural progenitor B cell Mast cell
9832 12245 4125 366 2206 430 netVisual_chord_gene(cor, sources.use = c(4), targets.use = c(1:12), lab.cex = 0.5, legend.pos.y = 30)
netVisual_chord_gene(cor, sources.use = c(1:12), targets.use = c(4), lab.cex = 0.5, legend.pos.y = 30)
#指定pathway
pathways.show <- c("TGFb") #SMC2
# Hierarchy plot
vertex.receiver = c(2,3,6,7,13) # a numeric vector.
netVisual_aggregate(cor, signaling = pathways.show, layout = "hierarchy", vertex.receiver = vertex.receiver)
netVisual_aggregate(cor, signaling = pathways.show, layout = "circle")
# netVisual_aggregate(cor, signaling = pathways.show, layout = "chord")
# Heatmap
netVisual_heatmap(cor, signaling = pathways.show, color.heatmap = c("#f1f1f1", "#ff2121"))#可视化单个配体受体对的影响
netAnalysis_contribution(cor, signaling = pathways.show)
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IHZlcnRleC5yZWNlaXZlcikKbmV0VmlzdWFsX2FnZ3JlZ2F0ZShjb3IsIHNpZ25hbGluZyA9IHBhdGh3YXlzLnNob3csIGxheW91dCA9ICJjaXJjbGUiKQojIG5ldFZpc3VhbF9hZ2dyZWdhdGUoY29yLCBzaWduYWxpbmcgPSBwYXRod2F5cy5zaG93LCBsYXlvdXQgPSAiY2hvcmQiKQoKIyBIZWF0bWFwCm5ldFZpc3VhbF9oZWF0bWFwKGNvciwgc2lnbmFsaW5nID0gcGF0aHdheXMuc2hvdywgY29sb3IuaGVhdG1hcCA9IGMoIiNmMWYxZjEiLCAiI2ZmMjEyMSIpKQpgYGAKCgpgYGB7cixmaWcuaGVpZ2h0PTYsZmlnLndpZHRoPTZ9CiPlj6/op4bljJbljZXkuKrphY3kvZPlj5fkvZPlr7nnmoTlvbHlk40KbmV0QW5hbHlzaXNfY29udHJpYnV0aW9uKGNvciwgc2lnbmFsaW5nID0gcGF0aHdheXMuc2hvdykKcGFpckxSLlRORiA8LSBleHRyYWN0RW5yaWNoZWRMUihjb3IsIHNpZ25hbGluZyA9IHBhdGh3YXlzLnNob3csIGdlbmVMUi5yZXR1cm4gPSBGQUxTRSkKTFIuc2hvdyA8LSBwYWlyTFIuVE5GWzEsXSAjIHNob3cgb25lIGxpZ2FuZC1yZWNlcHRvciBwYWlyIFRORjEyX0NYQ1I0CiMgSGllcmFyY2h5IHBsb3QKCm5ldFZpc3VhbF9pbmRpdmlkdWFsKGNvciwgc2lnbmFsaW5nID0gcGF0aHdheXMuc2hvdyxsYXlvdXQgPSAiaGllcmFyY2h5IiwgIHBhaXJMUi51c2UgPSBMUi5zaG93LCB2ZXJ0ZXgucmVjZWl2ZXIgPSB2ZXJ0ZXgucmVjZWl2ZXIpCm5ldFZpc3VhbF9pbmRpdmlkdWFsKGNvciwgc2lnbmFsaW5nID0gcGF0aHdheXMuc2hvdywgcGFpckxSLnVzZSA9IExSLnNob3csIGxheW91dCA9ICJjaXJjbGUiKQpuZXRWaXN1YWxfaW5kaXZpZHVhbChjb3IsIHNpZ25hbGluZyA9IHBhdGh3YXlzLnNob3csIHBhaXJMUi51c2UgPSBMUi5zaG93LCBsYXlvdXQgPSAiY2hvcmQiKQoKCgojIExSLnNob3cgPC0gcGFpckxSLlRORls0LF0gIyBzaG93IG9uZSBsaWdhbmQtcmVjZXB0b3IgcGFpciBUTkYxMl9DWENSNAojIEhpZXJhcmNoeSBwbG90Cm5ldFZpc3VhbF9pbmRpdmlkdWFsKGNvciwgc2lnbmFsaW5nID0gcGF0aHdheXMuc2hvdyxsYXlvdXQgPSAiaGllcmFyY2h5IiwgIHBhaXJMUi51c2UgPSBMUi5zaG93LCB2ZXJ0ZXgucmVjZWl2ZXIgPSB2ZXJ0ZXgucmVjZWl2ZXIpCm5ldFZpc3VhbF9pbmRpdmlkdWFsKGNvciwgc2lnbmFsaW5nID0gcGF0aHdheXMuc2hvdywgcGFpckxSLnVzZSA9IExSLnNob3csIGxheW91dCA9ICJjaXJjbGUiKQpuZXRWaXN1YWxfaW5kaXZpZHVhbChjb3IsIHNpZ25hbGluZyA9IHBhdGh3YXlzLnNob3csIHBhaXJMUi51c2UgPSBMUi5zaG93LCBsYXlvdXQgPSAiY2hvcmQiKQoKCm5ldFZpc3VhbF9idWJibGUoY29yLCBzb3VyY2VzLnVzZSA9IDgsIHRhcmdldHMudXNlID0gYygyLDMsNiw3LDEzKSwgcmVtb3ZlLmlzb2xhdGUgPSBGQUxTRSkgI1RfY2VsbDIg5L2c5Li6c291cmNlCiMgbmV0VmlzdWFsX2J1YmJsZShjb3IsIHNvdXJjZXMudXNlID0gNCwgdGFyZ2V0cy51c2UgPSBjKDE6NyksIHJlbW92ZS5pc29sYXRlID0gRkFMU0UpICNzdHJvbWFsIOS9nOS4unNvdXJjZQoKbmV0VmlzdWFsX2J1YmJsZShjb3IsIHNvdXJjZXMudXNlID0gNSwgdGFyZ2V0cy51c2UgPSBjKDIsMyw2LDcsMTMpLCBzaWduYWxpbmcgPSBjKCJUTkYiKSwgcmVtb3ZlLmlzb2xhdGUgPSBGQUxTRSkKYGBgCkFkZCBhIG5ldyBjaHVuayBieSBjbGlja2luZyB0aGUgKkluc2VydCBDaHVuayogYnV0dG9uIG9uIHRoZSB0b29sYmFyIG9yIGJ5IHByZXNzaW5nICpDdHJsK0FsdCtJKi4KCldoZW4geW91IHNhdmUgdGhlIG5vdGVib29rLCBhbiBIVE1MIGZpbGUgY29udGFpbmluZyB0aGUgY29kZSBhbmQgb3V0cHV0IHdpbGwgYmUgc2F2ZWQgYWxvbmdzaWRlIGl0IChjbGljayB0aGUgKlByZXZpZXcqIGJ1dHRvbiBvciBwcmVzcyAqQ3RybCtTaGlmdCtLKiB0byBwcmV2aWV3IHRoZSBIVE1MIGZpbGUpLgoKVGhlIHByZXZpZXcgc2hvd3MgeW91IGEgcmVuZGVyZWQgSFRNTCBjb3B5IG9mIHRoZSBjb250ZW50cyBvZiB0aGUgZWRpdG9yLiBDb25zZXF1ZW50bHksIHVubGlrZSAqS25pdCosICpQcmV2aWV3KiBkb2VzIG5vdCBydW4gYW55IFIgY29kZSBjaHVua3MuIEluc3RlYWQsIHRoZSBvdXRwdXQgb2YgdGhlIGNodW5rIHdoZW4gaXQgd2FzIGxhc3QgcnVuIGluIHRoZSBlZGl0b3IgaXMgZGlzcGxheWVkLgo=